Image information, be it color, black or white, is commonly generated in a bitmap format where the bitmap comprises a plurality of gray level pixels, i.e. pixels that are defined by digital values, each value representing a gray level among a number of gray levels. Thus, in an 8 bit system, 256 levels of gray are present, where each level represents an increment of gray between black and white. In the case of color bitmaps, where three defining colors or separations each include 256 levels of information, there may be more than 16 million colors defined by a gray bitmap.
Usually, bitmaps in such a gray level format are unprintable by standard printers. Standard printers print in a limited number of levels, either a spot or a no spot in the binary case, or a limited number of levels associated with the spot, for example, four in the quaternary case. Accordingly, it is necessary to reduce the gray level image data to a limited number of levels so that it is printed. Besides gray level information derived by scanning, certain processing techniques such as those described, for example, in U.S. patent application Ser. No. 07/600,542 entitled "Method for Image Conversions With Error Diffusion", by R. Eschbach, produce gray level pixel values which require conversion to a limited set of "legal" or output values.
One standard method of converting gray level pixel values to binary level pixel values is through the use of dithering or halftoning processes. In such arrangements, over a given area having a number of gray pixels therein, each pixel value of an array of gray level pixels within the area is compared to one of a set of preselected thresholds (the thresholds are stored as a dither matrix and the repetitive pattern generated by this matrix is considered a halftone cell) as taught, for example, in U.S. Pat. No. 4,149,194 to Holladay. The effect of such an arrangement is that, for an area where the image is gray, some of the thresholds within the dither matrix will be exceeded, i.e. the image value at that specific location is larger than the value stored in the dither matrix for that same location, while others are not. In the binary case, the pixels or cell elements for which the thresholds are exceeded might be printed as black, while the remaining elements are allowed to remain white, dependent on the actual physical quantity described by the data. The effect of the distribution of black and white over the halftone cell is integrated by the human eye as gray. Dithering or halftoning presents problems, however, in that the amount of gray within an original image is not maintained exactly over an area, because the finite number of elements inside each dither matrix--and therefore halftone cell--only allows the reproduction of a finite number of gray levels, i.e. number of elements in the cell plus one, or less. The error arising from the difference between the output pixel value and the actual gray level pixel value at any particular cell is simply thrown away. This results in a loss of image information. In particular, dithering introduces coarse quantization artifacts which are visible in the image areas where the scene has little variation. This is also known as "banding", and is caused by the limited number of output gray levels available. The "banding" artifacts generally increase with decreasing cell size, which is identical to a decrease in the number of levels that can be represented by the halftone cell.
In the ARIES (Alias Reduction and Image Enhancement System) method of halftone reproduction, described by P. Roetling in "Halftone Method With Edge Enhancement and Moire' Suppression," J. Opt. Soc. Amer. Vol. 66, No. 10, pp. 985-989, October, 1976, image information initially has a set of halftone screen values for a cell added to the information. A uniform threshold value is applied to the screened information, to produce an output value. The average gray value over the cell area of the input image is compared to the average gray value over the cell area of the output image. See, also, U.S. Pat. No. 4,051,536 to Roetling and U.S. Pat. No. 4,633,327 to Roetling. In this way, the error between original and output is minimized over each halftone cell. The banding artifact, however, is not reduced.
Algorithms that convert gray images to binary or other number of level images attempting to preserve the local density exist, and include among them error diffusion, as taught, for example, in "An Adaptive Algorithm for Spatial Greyscale" by Floyd and Steinberg, Proceedings of the SID 17/2, 75-77 (1976) (hereinafter, "Floyd and Steinberg"). Another, more elaborate method would be the error diffusion techniques of U.S. Pat. No. 5,045,952 to Eschbach, assigned to the same assignee as the present invention; which serves to provide image dependent edge enhancement. Additional modifications to the error diffusion algorithm taught by Floyd and Steinberg have been proposed, e.g.: a different weighting matrix, as taught, for example, in "A Survey of Techniques for the Display of Continuous Tone Pictures on Bilevel Displays" by Jarvis et al., Computer Graphics and Image Processing, Vol. 5., pp. 13-40 (1976) (hereinafter, Jarvis), and in "MECCA--A Multiple-Error Correction Computation Algorithm for Bi-Level Image Hardcopy Reproduction" by Stucki, IBM Res. Rep. RZ1060 (1981) (hereinafter, Stucki). Modifications of the error calculation and weight allocation have been taught, for example, in U.S. patent application Ser. No. 07/672,987, entitled "Method for Image Conversion with Application of Multiple Error Diffusion Matrices", by Eschbach, U.S. Pat. No. 4,924,322 to Kurosawa et. al., U.S. Pat. No. 4,339,774 to Temple, and U.S. Pat. No. 4,955,065, to Ulichney.
Error diffusion attempts to maintain image density by making the conversion from gray pixels to binary or other level pixels on a pixel-by-pixel basis. The procedure examines each pixel with respect to a threshold, and the difference between the gray level pixel value and the output value is forwarded to a selected group or set of neighboring pixels, in accordance with a weighting scheme. A problem noted with the use of the standard error diffusion algorithms for printing applications is the production of large numbers of isolated black and/or white pixels which are difficult to reproduce with many types of printers, including electrophotographic printers. An error diffusion method on halftone cell basis to overcome the printability problem is taught by U.S. Pat. No. 4,654,721 to Goertzel, where a method is shown to convert a continuous tone image to a bilevel pixel image. The total error generated in one halftone cell is distributed to a predetermined number of adjacent halftone cells. In this way, printable images are generated, while the banding artifact is reduced, by alternating between fixed output cell patterns. Because of an inherent lack of partial dots in this process, evidenced as a loss in sharpness, edge detection and sharpening was included. See, also, "Digital Halftoning in the IBM 4250 Printer" by Goertzel et al. (Goertzel), IBM J. Res. Develop., Vol 31, No. 1, January, 1987. U.S. patent application Ser. No. 07/583,337 to Shiau, and Ser. No. 07/775,201 to Fan, teach the use of similar methods to reduce a continuous tone image to a multilevel pixel image with diffusion of error between adjacent halftone cells. These methods, however, do not determine a best fit halftone dot as done in the ARIES method.
U.S. patent application Ser. No. 07/800,811 by Eschbach describes an error diffusion method that operates on a pixel basis where the threshold of the error diffusion process is modified in order to obtain printable patterns that resemble halftone cells by generating regular dot placements.
"MECCA--A Multiple-Error Correction Computation Algorithm for Bi-Level Image Hardcopy Reproduction" by Stucki, IBM Res. Rep. RZ1060 (1981), also describes an error diffusion algorithm incorporating actual printer dot overlaps in the error calculation, thereby generating a better printable result.
U.S. Pat. No. 5,055,943 to Levien suggests another pixel based error diffusion scheme where the tendency of the individual dots to form clusters in a screened image can be varied by applying a hysteresis constant and recursion techniques known from adaptive screening, to allow adjustment of image coarseness by adjustment of the hysteresis constant. This method produces better images, particularly for electrophotographic printing than the original error diffusion algorithm, but the images tend to have reduced sharpness or detail resolution, as compared to Floyd and Steinberg. In implementation, the Levien method uses an error diffusion process, providing a feedback response based on the output image, and particularly, dot size. The resulting irregular placement of dots improves the number of gray shades which can be reproduced. However, the hysteresis function implemented to control the feedback response tends to dampen the response at edges.
For example, U.S. Pat. No. 4,625,222 to Bassetti et al. discloses a print enhancement control system for an electrostatic copying machine wherein control logic circuitry processes a set of image altering parameters to improve image production quality. These parameters, whose values are either predetermined, fixed or have been received from an exterior source, improve image quality (i.e., resolution) by modifying modulated gray signals.
U.S. Pat. No. 4,700,229 to Herrmann et al. discloses an image enhancement circuit which converts a low quality image signal into a high quality image signal by modifying the binary representation of a picture. Image enhancement is accomplished by multiplying a series of error difference signals by a series of weighting factors k(i) which produce a clearer image by improving picture resolution.
U.S. Pat. No. 4,672,463 to Tomohisa et al. discloses a method to improve image quality within an electrostatic reproduction machine wherein the sharpness of an image is improved based on the value of an image sharpness control parameter that has been calculated examining the copy quality of an original.
U.S. Pat. No. 4,709,250 to Takeuchi discloses an image forming apparatus which improves the halftone image quality of an original. The pulse width of a reference control signal controls and improves image quality in response to a detected image density signal.
U.S. Pat. No. 4,724,461 to Rushing discloses an image improving process control for an electrostatic copying machine which maintains high image quality by adjusting a set of process control parameters.
U.S. Pat. No. 4,256,401 to Fujimura et al. discloses an image density adjustment method wherein a predetermined image density level within an electrostatic copying machine is maintained at a standard density by varying a set of input control parameters.
U.S. Pat. No. 4,693,593 to Gerger discloses a method of improving the image quality by controlling a single process parameter in response to changes in sensitometric characteristics.
All of the references cited herein are incorporated by reference for their teachings.